Seasonal Photosynthesis and Carbon Assimilation of Dynamics in a Zelkova Serrata (Thunb.) Makino Plantation

Article Seasonal Photosynthesis and Carbon Assimilation of Dynamics in a Zelkova serrata (Thunb.) Makino Plantation

Chung-I Chen 1 , Ya-Nan Wang 2, Heng-Hsun Lin 3, Ching-Wen Wang 4 , Jui-Chu Yu 3 and Yung-Chih Chen 1,3,*

1 Department of Forestry, National Chung Hsing University, No. 145, Xingda Rd., Taichung 40227, Taiwan; [email protected] 2 School of Forestry and Resource Conservation, College of Bio-Resources and Agriculture, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; [email protected] 3 The Experimental Forest, College of Bioresources and Agricultural, National Taiwan University, No. 12, Sec. 1, Qianshan Rd., Zhushan Township, Nantou County 55750, Taiwan; [email protected] (H.-H.L.); [email protected] (J.-C.Y.) 4 Endemic Species Research Institute, No. 1, Minsheng E. Rd., Jiji Township, Nantou County 55244, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-492-612-210

Abstract: As anthropogenic greenhouse gas emissions intensify global climate change, plantations

have become an important tool to mitigate atmospheric CO2. Our aim in this study was to estimate carbon assimilation and clarify the impact of environmental factors on the photosynthesis of Zelkova serrata (Thunb.) Makino, an important plantation species that is extensively planted in low altitude regions of East Asia. We measured monthly gas exchange parameters and leaf area index to estimate carbon assimilation. The results showed that gas exchange was significantly affected by vapor pressure deficit and temperature, especially in the dry season, and both photosynthetic rate Citation: Chen, C.-I.; Wang, Y.-N.; and carbon assimilation decreased. Lower daytime assimilation and higher nighttime respiration Lin, H.-H.; Wang, C.-W.; Yu, J.-C.; during the dry season, which caused a 43% decrease in carbon assimilation in Z. serrata plantations. Chen, Y.-C. Seasonal Photosynthesis Z. serrata exhibited lower photosynthetic rate and lower carbon assimilation following planting in a and Carbon Assimilation of tropical monsoon climate area. Therefore, the effects of extreme weather such as high temperature Dynamics in a Zelkova serrata (Thunb.) and vapor pressure deficit on Z. serrata forest carbon budget could be stronger in the future. Leaf Makino Plantation. Forests 2021, 12, area showed seasonal variation, and severe defoliation was caused by a typhoon in the summer. The 467. https://doi.org/10.3390/ annual carbon assimilation was estimated at 3.50 Mg C ha−1 year−1 in the study area. f12040467

Keywords: canopy; leaf area index; photosynthetic rate; vapor pressure deﬁcit; water deﬁcit Academic Editor: ZdenˇekVacek

Received: 12 March 2021 Accepted: 9 April 2021 Published: 11 April 2021 1. Introduction Forests are the world’s largest terrestrial carbon sink and can sequester CO2 from the Publisher’s Note: MDPI stays neutral atmosphere, modify atmospheric components, mitigate greenhouse effects, and provide with regard to jurisdictional claims in ecosystem services such as provisioning of fodder and timber and non-timber products published maps and institutional affil- that facilitate sustainable livelihoods. Therefore, a profound understanding of forest iations. carbon budgets under extreme weather conditions is required urgently [1,2]. However, plantations are subject to various environmental stresses in the field that may impact their photosynthetic efficiency, growth performance, and carbon sequestration potential [3,4]. For example, environmental factors such as precipitation and soil water content may not Copyright: © 2021 by the authors. be the major factors affecting carbon assimilation, whereas vapor pressure deficit and Licensee MDPI, Basel, Switzerland. warming could strongly impact forest carbon sinks under drought conditions [5,6]. This article is an open access article Seasonality drives cycles of plant productivity in several ecosystems, and seasonal distributed under the terms and variation of photosynthesis and respiration are essential for ecosystem-atmosphere CO2 conditions of the Creative Commons exchange [7,8]. For example, photosynthetic light-use efficiency peaks in spring and then Attribution (CC BY) license (https:// declines due to leaf aging or water stress, and night respiration gradually decreases during creativecommons.org/licenses/by/ the growing season [7]. The impact of extreme weather on forests is becoming increasingly 4.0/).

Forests 2021, 12, 467. https://doi.org/10.3390/f12040467 https://www.mdpi.com/journal/forests Forests 2021, 12, 467 2 of 13

severe under climate change, especially in seasonally dry ecosystems, due to cycles of prolonged drought and heavy rain. Drought is the primary cause of stress in forest ecosystems under climate change and may increase tree mortality under climatic warming [6]. In 2011, an intense drought impacted several regions around the world, causing a reduction in forest carbon sequestration [9]. Drought during the early growth season has long-lasting effects on photosynthesis and ecosystem carbon balance in plantations [3]. Severe drought suppressed tree photosynthesis more than respiration, leading to a reduction of 0.23–0.53 Pg of carbon in the Amazon Basin, turning the forest from a carbon sink to a source within a short period [10]. Drought usually accompanies high temperature and vapor pressure deficit, which influence forest carbon balance. For example, increasing vapor pressure deficit under climatic warming can reduce forest CO2 uptake regardless of changes in soil water content, therefore warming may be the main climate factor that impacts carbon storage in tropical dry region [5,6]. Consequently, the influences of environmental factors on forest carbon balance require detailed multisite investigation [10]. Additionally, the forest response to changing environmental conditions may affect the regional and global climate [6]. Determining plantation species that are adapted to regional environmental conditions is necessary because of both limited plantation areas and climate change. In this study, we measured photosynthesis-related parameters to estimate the carbon assimilation of Zelkova serrata (Thunb.) Makino, an important plantation species in East Asia with high woody carbon content and economical value. Z. serrata mainly grows in the river valleys and sparse forest habitats beside streams, preferring fertile, moisture-laden, and well-drained soil conditions [11,12]. In addition, it exhibits considerable variation in growth performance under different growth site environments. Temperature seasonality, annual precipitation, annual mean temperature, and precipitation of the warmest quarter have been reported to be the key factors influencing its natural distribution in China [11]. The aims of the present study were to clarify the seasonal dynamic on photosynthesis and carbon assimilation dynamics of Z. serrata plantations and identify the key environmental factors influencing its growth performance under tropical monsoon climates.

2. Materials and Methods 2.1. Study Site and Species The data for this study were collected in 2011 at Wan-Long Farm, a lowland former sugarcane plantation owned by the Taiwan Sugar Corporation in Sinpi Township, Pingtung, Taiwan (120◦3603000 E, 22◦3102600 N, 69 m above sea level). The soils are classified as Entisols with more than 60% sandstone parent material and 45%–55% gravel content, and the soil profile is shallow (<40 cm depth) [12,13]. In the study site, 14 species were used for afforestation from 2002 to 2005, with Z. serrata widely planted near the center of the farm in 2003. Z. serrata is a pioneer species frequently used in lowland afforestation, with the second largest plantation area in Taiwan. The initial stand density was 1500 seedlings ha−1 (2.5 × 2.5 m tree spacing), with constant pruning and weed removal. Mean diameter at breast height was calculated in a 20 × 25 m area in 2010, which was 3.89 ± 1.58 cm (N = 27). Mean tree height was 3.46 ± 1.28 m (N = 27). The surrounding area of the study plot had the same species and similar size, which had 77.4% of crown density on average. Microclimate data were collected at a microclimate station 400 m southeast of experimental plot from 2009 to 2011, which had a rain gauge and a temperature and relative humidity probe (HMP45C, Vaisala, Finland). The region has a typical tropical monsoon climate, with a high frequency of typhoons and afternoon thundershowers during summer. However, the annual precipitation in 2011 was the lowest (1929 mm) during the study period and represented an intense drought event. In 2011, the January and June mean air temperature were 16.6 and 28.0 ◦C, respectively; annual precipitation was concentrated from May to September (wet season). The monthly precipitation, soil water content, and average maximum temperature variation in 2011 are shown in Figure1. Data on soil water content were collected by the microclimate station using a time-domain reflectometer Forests 2021, 12, 467 3 of 13

climate, with a high frequency of typhoons and afternoon thundershowers during summer. However, the annual precipitation in 2011 was the lowest (1929 mm) during the study period and represented an intense drought event. In 2011, the January and June mean air temperature were 16.6 and 28.0 °C, respectively; annual precipitation was con- Forests 2021, 12, 467 centrated from May to September (wet season). The monthly precipitation, soil water con- 3 of 13 tent, and average maximum temperature variation in 2011 are shown in Figure 1. Data on soil water content were collected by the microclimate station using a time-domain reflectometer(TDR, (TDR, CS616, CS616, Campbell Campbell Scientiﬁc Scientific Inc., Inc., Logan, Logan, UT, UT, USA) USA) installed installed 20 cm 20 underground, cm under- and ground,showed and showed a pattern a pattern similar similar to that ofto precipitation,that of precipitation, being the being lowest the in lowest March in (8.8%) March and the (8.8%) highestand the inhighest July (19.4%) in July in(19.4%) 2011. in 2011.

600 25 Precipitation

Tmax 500 soil water content 35 20

400

30 C) 300 o 15 ( a T

Precipitation (mm) Precipitation 200

25 soil water content (%) 10

100

20 0 5 024681012 Months (2011)

Figure Figure1. The monthly 1. The monthly average average maximum maximum temperature temperature (Tmax), soil (Tmax water), soil content, water content, and accumulated and accumulated precipitationprecipitation in 2011. in 2011. 2.2. Measurements of Gas Exchange and Leaf Area 2.2. Measurements of Gas Exchange and Leaf Area The crowns of shade-intolerant species, such as Z. serrata, are usually mono-level of The crowns of shade-intolerant species, such as Z. serrata, are usually mono-level of canopies, which is indicated by an leaf area index (LAI) = 1.07, and monthly variation in canopies, which is indicated by an leaf area index (LAI) = 1.07, and monthly variation in the 0.4–2.1 range in 2011. To enable convenient transportation of instruments, we selected the 0.4–2.1 range in 2011. To enable convenient transportation of instruments, we selected three nearby trees for gas exchange measurements. Scaffolds (1.7 m height) were set near the three nearby trees for gas exchange measurements. Scaffolds (1.7 m height) were set near sample trees with tripods so the leaf clamp reached the canopy leaves. Diurnal variations in the sample trees with tripods so the leaf clamp reached the canopy leaves. Diurnal varia- leaf gas exchange were measured using portable photosynthesis systems (LI-6400; LI-COR, tions in leaf gas exchange were measured using portable photosynthesis systems (LI-6400; Lincoln, NE, USA) with a clear chamber top (LI-6400-08; LI-COR) one day per month for LI-COR, Lincoln, NE, USA) with a clear chamber top (LI-6400-08; LI-COR) one day per each sample tree in 2011 from three intact, fully expanded mature leaves on the same side of month for each sample tree in 2011 from three intact, fully expanded mature leaves on the the canopy. The 5–7th leaf from the top was selected for measurement to prevent sampling same side of the canopy. The 5–7th leaf from the top was selected for measurement to juvenile and aging leaves. The sampled leaves were fully placed in the 2 × 3 cm leaf chamber prevent sampling juvenile and aging leaves. The sampled leaves were fully placed in the of the instrument. Measurements were taken hourly from 8:00 a.m. to 4:00 p.m. (mean solar 2 × 3 cm leaf chamber of the instrument. Measurements were taken hourly from 8:00 a.m. time), and net photosynthesis rate (Pn), stomatal conductance (gs), transpiration rate (E), and to 4:00 p.m. (mean solar time), and net photosynthesis rate (Pn), stomatal conductance (gs), −1 intercellular CO2 concentration (Ci) were recorded. The flow rate was set to 500 µmol s transpirationand the rate air inlet(E), and of the intercellular LI-6400 was CO connected2 concentration to a plastic (Ci) tubewere(2–3 recorded.m in length), The flow with the rate was set to 500 µmol s−1 and the air inlet of the LI-6400 was connected to a plastic tube end set away from the operator to prevent the influence of human activities, and the CO2 (2–3 mconcentration in length), with in the the leaf end chamber set away was from maintained the operator at the level to prevent of the ambient the influence atmosphere. of The human activities, and the CO2 concentration in the leaf chamber was maintained at the CO2 sample concentrations ranged from 382.4 to 395.9 ppm during max photosynthesis in level of the ambient atmosphere. The CO2 sample concentrations ranged from 382.4 to summer. The air temperature (Ta), leaf temperature (Tl), vapor pressure deficit (VPDa), and 395.9 ppm during max photosynthesis in summer. The air temperature (Ta), leaf temper- leaf vapor pressure deficit (VPDl) were also recorded. We represented four days of diurnal ature (Tdatal), vapor in January pressure 28, May deficit 31, (VPD Septembera), and 4, leaf and vapor November pressure 25 from deficit monthly (VPDl) measurements were also in recorded.2011. We Photosynthetic represented photonfour days flux of density diurna (PPFD)l data in and January VPD were 28, May measured 31, September on the leaves. 4, The and Novemberdata gap in25 Septemberfrom monthly 4 was measurements caused by rain in in 2011. the afternoon. Photosynthetic photon flux density (PPFD)The and photosynthesis VPD were measured response on to the different leaves. light The intensitydata gap gradientsin September was measured4 was to causedconstruct by rain in photosynthetic the afternoon. light response curves. Measurements were taken for three leaves per sampled tree in each season using an LI-6400 with an artiﬁcial LED light chamber (LI- 6400-02B; LI-COR). The leaf selection criteria were the same for the diurnal measurement. Sample leaves were exposed to 500 µmol m−2 s−1 artiﬁcial PPFD for a few minutes before

measurements were taken to induce leaf stomatal opening. The artiﬁcial light intensities were set at 0, 5, 10, 20, 50, 100, 200, 500, 750, 1000, 1500, and 2000 µmol m−2 s−1 in sequence with about 10–20 min of stabilization time under each light intensity. Seasonal measurements of photosynthetic light response curves were used to calculate maximum assimilation rate (Amax) and shape parameter (θ). Simultaneously, quantum efﬁciency (α) Forests 2021, 12, 467 4 of 13

and dark respiration rate (Rd) were calculated from the initial slope of the photosynthetic light response curve, at light intensities lower than 50 µmol m−2 s−1 [14,15]. Tree total leaf area was estimated from the LAI, measured monthly at dusk from the top and under the canopy in three sample trees [15,16]. LAI was measured using a plant canopy analyzer (LAI-2200; LI-COR) in four directions under each sample tree. The sensor was covered with a lens cap (90◦ opening) to prevent overestimation of the canopy leaf area owing to shading by the main branch and trunk. The total leaf area of each sample tree was calculated as LAI multiplying canopy projected area of an individual tree’s canopy. The canopy projected area was calculated as an ellipse area following measurement of the major and minor axis in two perpendicular directions as crown projection.

2.3. Data Processing and Analysis According to several studies, the light response curve can demonstrate the effects of environmental factors such as nutrient levels, temperature, and water variables on leaf photosynthesis [16–18]. Therefore, the leaf carbon assimilation (A, µmol m−2 s−1) was calculated from the following equation:

2αIl /Amax A(Amax, Il) = Amax r (1) α α 2 α 1 + Il + 1 + Il − 4θ Il Amax Amax Amax

–2 –1 where Amax (µmol m s ) is the maximum net assimilation of CO2, α is the quantum –2 –1 efﬁciency, Il (µmol m s ) represents leaf-level light intensity, which is derived from the measurements at the canopy top according to Beer’s Law, and θ is the shape parameter of the photosynthetic light response curve, calculated as the slope of the tangent at the light saturation point. The associated parameters are listed in Table1. The model has been well-tested and extensively applied in several leaf carbon assimilation studies [14,16–18]. In addition, the model is used as upscaling from leave to canopy level estimation by considering the light extinction in canopy.

Table 1. Average values (± standard error) of quantum efﬁciency (α), shape parameter (θ), maximum

net assimilation rate (Amax), and dark respiration rate (Rd) of Zelkova serrata (Thunb.) Makino across different seasons.

Winter Spring Summer Autumn α (mol·mol−1) 0.038 ± 0.007 0.041 ± 0.001 0.036 ± 0.006 0.032 ± 0.008 θ 0.004 ± 0.001 0.005 ± 0.001 0.005 ± 0.001 0.006 ± 0.002 –2 –1 Amax (µmol m s ) 0.92 ± 0.43 3.01 ± 0.78 4.99 ± 1.21 5.57 ± 1.94 –2 –1 Rd (µmol m s ) 3.96 ± 0.75 2.43 ± 0.55 1.79 ± 0.30 0.94 ± 0.25

Several models to scale-up carbon assimilation from leaf to canopy level are already in use [14,16,18]. After calculating the leaf assimilation rate, we upscaled from leaf- to canopy-level assimilation using LAI as shown in [16]. According to [16,18] the estimation formulas were as follow: h L Z Z Ac = A(Amax, Il) dLdt (2) 0 0 n L Z Z Rc = RddLdt (3) 0 0

Total daily canopy assimilation (Ac) can be calculated by considering total leaf area (L), day length (h), and leaf carbon assimilation (A) from Equation (1). Total night canopy respiration (Rc) can be calculated by considering total leaf area (L), night length (n), and dark respiration rate (Rd) from light response curves. The differences in mean temperature between day and night in the study period were less than 3 and 4 ◦C in winter and Forests 2021, 12, 467 5 of 13

summer, respectively. Additionally, we calculated monthly assimilation and respiration by considering days in each month, and annual carbon assimilation of Z. serrata was calculated by summing up monthly carbon assimilations. SAS 9.4. statistical software (SAS Inc., Cary, NC, USA) was used to analyze the Pearson correlation between gas exchange and environmental variables. Correlation analysis data were collected from monthly measurement of diurnal variation. Signiﬁcance was set at p < 0.05. Data in ﬁgures are presented as means ± standard error.

3. Results In the study area, precipitation from May−September was 94%, 92%, and 83% of the accumulated annual precipitation in 2009, 2010, and 2011, respectively. Soil water content was the lowest in March (8.8%) and the highest in July (19.4%) in 2011. Therefore, data from the dry and wet seasons were analyzed separately. The correlations between gas exchange parameters and environmental factors are shown in Table2. The data showed a significant correlation between gs and E during dry and wet seasons but not between Pn and gs. The results demonstrated that the leaf transpiration rate was controlled by stomatal closure, but the decrease in Pn may be caused by non-stomatal limitation. In both dry and wet seasons, Pn had no significant correlation with PPFD, but showed significant negative correlations with VPD and temperature in the dry season (Table2). VPD greatly influenced stomatal opening/closure, especially under dry and high temperature conditions. At the study site, the dry season was over six months and the highest VPD (up to 5.14 kPa) occurred at noon in March during 2011, demonstrating its impact on plant CO2 assimilation.

Table 2. Correlation coefficients for Zelkova serrata (Thunb.) Makino between gas exchange parameters (Pn: net photosynthetic rate, gs: stomata conductance, Ci: intercellular CO2 concentration, E: transpiration rate) and environmental factors (VPDl: leaf to air vapor pressure deficit, VPDa: vapor pressure deficit, Tl: leaf temperature, Ta: air temperature, CO2: ambient CO2 concentration, PPFD: photosynthesis photon flux density) in dry (gray) and wet (white) season.

Pn gs Ci E VPDl VPDa Tl Ta CO2 PPFD Pn - 0.14 −0.55 ** 0.03 −0.30 ** −0.33 ** −0.25 ** −0.29 ** −0.08 0.05 gs −0.11 - 0.34 ** 0.81 ** −0.32 ** −0.25 ** −0.13 −0.08 −0.10 −0.04 Ci −0.86 ** 0.28 - 0.26 ** −0.14 −0.05 0.00 0.09 0.15 −0.25 ** E 0.22 0.77 ** 0.02 - 0.16 0.21 * 0.30 ** 0.35 ** −0.23 ** 0.25 ** VPDl 0.19 −0.56 ** −0.36* −0.12 - 0.93 ** 0.83 ** 0.80 ** −0.36 ** 0.56 ** VPDa 0.14 −0.50 ** −0.33* −0.18 0.87 ** - 0.87 ** 0.88 ** −0.35 ** 0.43 ** Tl 0.20 0.06 −0.30* 0.37 ** 0.60 ** 0.63 ** - 0.96 ** −0.56 ** 0.28 ** Ta 0.15 0.12 −0.26 0.39 ** 0.51 ** 0.60 ** 0.97 ** - −0.48 ** 0.26 ** CO2 0.05 −0.59 ** −0.11 −0.56 ** 0.35 * 0.43 ** −0.29 * −0.32 * - −0.12 PPFD 0.14 −0.07 −0.17 0.29 * 0.61 ** 0.32 * 0.40 ** 0.29 * −0.08 - *, ** means p < 0.05 and p < 0.01. N = 138 (dry season), N = 49 (wet season).

Diurnal variations in PPFD and VPD on leaves in different seasons are illustrated in Figure2 and gas exchange variation is shown in Figure3. Diurnal variations in gas exchange represent the ﬂuctuations in gas exchange during the day and the recovery overnight. The net photosynthesis rate in the four seasons was high in the morning and −2 −1 decreased in the afternoon. In the morning, Pn reached 3.8 and 5.6 µmol m s in the dry and wet seasons, respectively. Forests 2021, 12, 467 6 of 13

Diurnal variations in PPFD and VPD on leaves in different seasons are illustrated in Figure 2 and gas exchange variation is shown in Figure 3. Diurnal variations in gas exchange represent the fluctuations in gas exchange during the day and the recovery over- Forests 2021, 12, 467 night. The net photosynthesis rate in the four seasons was high in the morning6 of 13 and decreased in the afternoon. In the morning, Pn reached 3.8 and 5.6 µmol m−2 s−1 in the dry and wet seasons, respectively.

FigureFigure 2. 2.Diurnal Diurnal variation variation of light of intensitylight intensity (PPFD) and(PPFD) vapor and pressure vapor deﬁcit pressure (VPDa )deficit on 28 January,(VPDa) on 28 Janu- 31ary, May, 31 4May, September, 4 September, and 25 November, and 25 November, 2011. N = 3, bars2011. indicate N = 3,± barsstandard indicate error. ± standard error.

The daytime CO2 assimilation, nighttime respiration, and LAI are shown in Figure4. The daytime CO2 assimilation, nighttime respiration, and LAI are shown in Figure 4. CO2 assimilation in the wet season was significantly higher than that in the dry season, with CO2 assimilation in the wet season was significantly higher than that in the dry season, the yearly maximum in September (160.9 g CO2) and the minimum in February (6.7 g CO2). Thewith daytime the yearly carbon maximum sink capacity in September of trees was lower(160.9 than g CO the2) nighttime and the respirationminimum fromin February (6.7 Januaryg CO2). toThe March. daytime In this carbon study, sink the singlecapacity tree of nighttime trees was respiration lower than ranged the nighttime from 14.6 respiration tofrom 85.6 January g CO2. Higher to March. dark respirationIn this study, and light the compensationsingle tree nighttime point values respiration with lower ranged from A and light saturation point were observed in the dry season. LAI represented the 14.6max to 85.6 g CO2. Higher dark respiration and light compensation point values with tree canopy dynamics and frequently fluctuated with seasons. LAI showed high values lower Amax and light saturation point were observed in the dry season. LAI represented in spring and summer, thus represented seasonal variation in Z. serrata. However, the experimentalthe tree canopy area was dynamics struck by and an intense frequently typhoon fluc intuated August, with causing seasons. serious LAI defoliation showed high val- andues lowin spring LAI. The and difference summer, between thus represented the fluctuations seasonal in leaf variation assimilation in andZ. serrata monthly. However, the COexperimental2 assimilation area was mainlywas struck caused by by an the intens monthlye typhoon variation in leafAugust, area (Figure causing5). Theserious defolia- monthlytion and leaf low assimilation LAI. The rate difference ranged from between 0.88 to 4.17 theµ molfluctuations m−2 s−1. Thein leaf single-tree assimilation and annual CO assimilation was 23.79 kg. Z. serrata carbon assimilation was estimated as monthly CO2 2 assimilation was mainly caused by the monthly variation in leaf area (Figure 3.50 Mg C ha−1 year−1 in the study area. CO assimilation in the dry season was 56% 5). The monthly leaf assimilation rate ranged2 from 0.88 to 4.17 µmol m−2 s−1. The single- that of the wet season, mainly caused by higher daytime assimilation and lower nighttime respirationtree annual in theCO wet2 assimilation season than was in the 23.79 dry season kg. Z. (Figure serrata6 ).carbon Results assimilation also indicated was that estimated as drought3.50 Mg suppressed C ha−1 year photosynthesis−1 in the study more area. than CO respiration.2 assimilation in the dry season was 56% that of the wet season, mainly caused by higher daytime assimilation and lower nighttime respiration in the wet season than in the dry season (Figure 6). Results also indicated that drought suppressed photosynthesis more than respiration.

ForestsForests 20212021, ,1212, ,467 467 7 of 13 7 of 13

Forests 2021, 12, 467 7 of 13

FigureFigure 3. 3.Diurnal Diurnal variation variation of netof net photosynthetic photosynthetic rate (ratePn), stomata(Pn), stomata conductance conductance (gs), and (g transpi-s), and transpi- rationFigureration rate 3.rate Diurnal (E) (E) on on 28variation 28 January, January, of 31 net May,31 photosynthetic May, 4 September, 4 September, rate and (P 25n ),and November,stomata 25 November, conductance 2011. N 2011.= (g 3,s bars), Nand = indicate 3,transpi- bars ±indicate ± standardrationstandard rate error. error.(E) on 28 January, 31 May, 4 September, and 25 November, 2011. N = 3, bars indicate ± standard error.

200 200 CS 2.5 CS 2.5 RD RD LAILAI 150 150 2.0 2.0 ) ) 2 2

1.5 1.5 100

100 LAI LAI

1.0 1.0 CS and RD (g CO

CS and RD (g CO 50 50 0.5 0.5

0 0.0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0.0 Jan Feb Mar Apr May2011 Jun Jul Aug Sep Oct Nov Dec 2011 FigureFigure 4.4. MonthlyMonthly daytimedaytime COCO22 assimilation (CS), night respiration (RD), and leaf area indexindex (LAI) ofofFigure ZelkovaZelkova 4. serrata serrataMonthly (Thunb.)(Thunb.) daytime Makino.Makino. CO2 N Nassimilation == 3, bars indicate (CS), ± night standardstandard respiration error. error. (RD), and leaf area index (LAI) of Zelkova serrata (Thunb.) Makino. N = 3, bars indicate ± standard error.

Forests 2021, 12, 467 8 of 13 Forests 2021, 12, 467 8 of 13

Forests 2021, 12, 467 8 of 13

FigureFigure 5.5. The monthly leaf leaf assimilation assimilation rate rate (A) (A) and and monthly monthly CO CO2 assimilation2 assimilation (Am) (Am) of Zelkova of Zelkova serrataserrata (Thunb.) Makino. Figure(Thunb.) 5. The monthly Makino. leaf assimilation rate (A) and monthly CO2 assimilation (Am) of Zelkova serrata (Thunb.) Makino.

Figure 6. Ratio of day CO2 assimilation (day CS) and night respiration (night RD) in wet and dry seasons. Figure 6. Ratio of day CO2 assimilation (day CS) and night respiration (night RD) in wet and dry Figure 6. Ratio of day CO2 assimilation (Day CS) and night respiration (Night RD) in wet and seasons. dry4. Discussion seasons. 4.4.1.4. DiscussionDiscussion Gas exchange and Environmental Factors 4.1.4.1. Gas GasThe exchange exchange 10-year and averageand Environmental Environmental annual precipitation Factors Factors was 2700.4 mm from 2005 to 2014 in southern Taiwan,TheThe 10-year 10-year thus average averagethe precipitation annual annual precipitation precipitation in 2011 (1929 was was mm) 2700.4 2700.4 was mm mm much from from lower 2005 2005 to than 2014to 2014 average. in southern in south- The Taiwan,annualern Taiwan, thusevaporation thethus precipitation the in precipitation 2011 (1756.9 in 2011 inmm) (1929 2011 was mm) (1929 measured was mm) much was by lower much the central than lower average. weather than average. The bureau annual The in evaporationsouthernannual evaporation Taiwan. in 2011 Plant (1756.9 in 2011 gas mm) exchange(1756.9 was mm) measured was was sensit measured byive the to centralenvironmental by the weather central bureaufactors weather such in southernbureau as light in Taiwan.intensity,southern Plant Taiwan.water gas availability, exchange Plant gas was exchangehumidity, sensitive was and to sensit temperature. environmentalive to environmental For factors example, such factorsa aswater light such deficit intensity, as lightmay watercauseintensity, availability,the reductionwater availability, humidity, of stomata humidity, and conductance, temperature. and temperature. photosynthetic For example, For rate,example, a water and transpiration,a deficit water may deficit cause lead- may theingcause reductionto negativethe reduction of effects stomata of onstomata plant conductance, growthconductance, [19–22]. photosynthetic ph Waterotosynthetic deficit rate, rate,is and likely and transpiration, to transpiration, be more severe leading lead- in totheing negative seasonalto negative effects drought effects on regions; plant on plant growth therefore, growth [19 –[19–22]. the22]. se Waterlection Water deficit of deficit plantation is likelyis likely species to to be be moreadapted more severe severe to such in in theenvironmentalthe seasonalseasonal drought stress regions;factors regions; is therefore, therefore,essential. the Diff the seerent selectionlection trees of ofplantationspecies plantation may species show species adapted different adapted to phys- such to suchiologicalenvironmental environmental responses stress to stress factorsmicroclimate factors is essential. is factors essential. Diff anderent Different their trees interaction treesspecies species mayin plantation show may show different areas. different phys-Diur- physiologicalnaliological variation responses of responses gas exchangeto microclimate to microclimate could be factors the factors indicator and their and of theirinteraction maintained interaction in photosynthetic plantation in plantation areas. capacity areas. Diur- Diurnalinnal various variation variation environmental of gas of exchange gas exchange conditions could couldbe [23–25]. the beindicator the indicator of maintained of maintained photosynthetic photosynthetic capacity capacity in various environmental conditions [23–25]. in variousThe diurnal environmental variation conditions of the net [23–25]. photosynth esis rate was high in the morning and The diurnal variation of the net photosynthesis rate was high in the morning and decreasedThe diurnal in the afternoon. variation Similarof the net results photosynth were foundesis byrate [26] was in highyoung in Cinnamomum the morning cam-and decreased in the afternoon. Similar results were found byn [26] in young Cinnamomum cam- phoradecreased and Z. in serratathe afternoon. in central Similar Taiwan. results High were values found of P by in [26] the in morning, young Cinnamomum which decreased cam- phoraafter andnoonZ. without serrata in recovery central Taiwan.until sunset, High were values shown of Pn occasionallyin the morning, in the which field decreased owing to phora and Z. serrata in central Taiwan. High values of Pn in the morning, which decreased after noon without recovery until sunset, were shown occasionally in the field owing to highafter lightnoon intensity, without recoveryair temperature, until sunset, and VPD were at shownnoon [27,28]. occasionally The decrease in the fieldin both owing Pn and to highgs through light intensity, the day airappears temperature, to be the and result VPD of atlight-driven noon [27,28 accumulations]. The decrease of in photosynthe- both Pn and high light intensity, air temperature, and VPD at noon [27,28]. The decrease in both Pn and gsiss through products the [29]. day However, appears to the be P then values result recovered of light-driven over accumulations the following morning, of photosynthesis showing gs through the day appears to be the result of light-driven accumulations of photosynthe- productsthat the damage [29]. However, to photosynthesis the Pn values under recovered environmental over the stress following is transient. morning, We found showing that sis products [29]. However, the Pn values recovered over the following morning, showing that the damage to photosynthesis under environmental stress is transient. We found gthats and the E damageshowed tosimilar photosynthesis diurnal fluctuation under enviro andnmental the values stress in the is transient.wet season We were found higher that gs and E showed similar diurnal fluctuation and the values in the wet season were higher

Forests 2021, 12, 467 9 of 13

that gs and E showed similar diurnal fluctuation and the values in the wet season were higher than those in the dry season. These results demonstrated that leaf transpiration was controlled by stomatal opening/closure in Z. serrata, as shown in Table1. Several studies also demonstrated a similar relationship between stomata and water loss during the dry and wet seasons [30,31]. The effects of stomatal or non-stomatal limitation on the reduction of photosynthetic rate can be determined by the correlation between Pn, gs, and Ci [32,33]. For example, the positive correlation between Pn and gs and the negative correlation between Pn and Ci were determined as the effect of stomata limitation. Stomata regulation can prevent excess water loss from leaves, but also limit the carbon uptake. The non-significant relation between Pn and gs and the significant relation between gs and E demonstrated that the maintenance of leaf water status under drought is more important than carbon uptake [29]. Both temperature and VPD presented a significant correlation with Pn in the dry season, showing that the responses of the leaves were directly affected by the high temperature and dry air. Several studies demonstrated that the influence of temperature and VPD on photosynthetic rate was occasionally larger than that of light intensity and soil water content. Thus, the variation of VPD and stomata conductance may influence leaf gas exchange and CO2 assimilation [6,21,34–37]. Harsh environmental conditions could become more severe in the future since climate change could intensify high temperature and drought conditions, causing higher VPD. Light intensity is one of the key factors influencing photosynthetic rate and, therefore, the forest carbon sink. The optimum range of light intensity is species-specific. However, the photosynthetic light response curve demonstrated the relationship between light intensity and Pn on leaves and could calculate the light compensation point, Amax,Rd, and α [32,38,39]. These parameters varied both with internal plant factors (e.g., shade tolerance) and external environmental factors (e.g., water stress) [32,40]. In addition, several studies have shown that these parameters can be used to calculate the mean leaf carbon assimilation rate and upscale to the daily carbon assimilation of the whole tree [4,16–18].

4.2. Leaf Carbon Assimilation and Leaf Area Index The mean leaf carbon assimilation rate can be upscaled to the tree and stand level by considering LAI. The results showed that the daytime carbon assimilation was lower than the nighttime respiration in the dry season. Several studies demonstrated that the photosynthetic rate was higher in the wet season than in the dry season [19,21,22,41]. Kuo et al. [40] showed that mean light-saturated photosynthetic rate and stomatal conductance measured in the rainy season increased 48% and 286%, respectively, when compared with the dry season in 30 broad-leaved tree species, demonstrating that stomatal opening was severely restricted during the dry season. Hu et al. [42] showed that the maximum photosynthetic rate may ﬂuctuate 2.2–3.1-fold under different light regimes in subtropical broad-leaved tree species. Moreover, the variation of photosynthetic rate under different environmental conditions also represented the variation of carbon sequestration potential. A higher Rd and light compensation point with lower Amax and light saturation point were observed in the dry season. Generally, the leaf respiration rate is greatly affected by temperature and light intensity and, hence, shows seasonal variation [43]. However, Yin et al. [32] demonstrated that drought impacted the pattern of the photosynthetic light response curve, which increased the dark respiration and light compensation point and decreased Amax and light saturation point. Therefore, the light range utilization was reduced, which decreased the photosynthetic potential. The leaf respiration in the dark is usually higher than in the light, however, the light suppression of respiration may reduce in high temperature conditions [43]. Plant respiration plays an important role in the global carbon cycle, with 120 billion tonnes of carbon sequestered by terrestrial plants compared with 60 billion tonnes of carbon released by plant respiration annually [43]. Additionally, severe drought and increasing temperatures in the future could suppress the photosynthesis and increase respiration in plantations under climate change. Forests 2021, 12, 467 10 of 13

Total leaf area not only inﬂuences the amount of light available but also affects the carbon sequestration of trees because it is the key factor for upscaling from leaf to stand level. High leaf area increases the amount of carbon sequestration by photosynthesis but also increases the amount of carbon emitted by respiration [44,45]. The variation of leaf area may be affected by internal factors such as the phenology of each species and external factors such as typhoons and tree pathology [45]. Biswas et al. [16] indicated that the LAI of seven young broadleaf tree species ranged from 1.22 to 3.26 in a plantation area. Chen et al. [46] found that the LAI of Sapindus mukorossi in the plain area ranged from 0.75 to 3.74. Compared with previous studies, the lower LAI (0.39–2.16) in Z. serrata at the study site may be caused by poor site quality, speciﬁc difference, and plant pathology [47,48].

4.3. Seasonal Carbon Assimilation

The leaf assimilation rate can be upscaled to single-tree monthly CO2 assimilation by considering total leaf area and day length (Figure5). The monthly leaf assimilation rate in Z. serrata was lower than that of S. mukorossi (1.18–9.10 µmol m−2 s−1) in Southern Taiwan [46] and seven broad-leaved tree species (1.37–11.61 µmol m−2 s−1) in Eastern India [16]. However, a previous study demonstrated that the poor site quality in the study area may be caused by a long-term monoculture of sugarcane [12]. In addition, many trees showed symptoms of sooty mold in this area. Therefore, the effects of pathology on leaf photosynthesis could be an interesting ﬁeld of research. The single-tree annual CO2 assimilation rates were lower than those of other studies −1 −1 in Taiwan such as 96.14 kg CO2 year in Z. serrata [26], 100 kg CO2 year in Phyllostachys −1 −1 pubescens [49], 230 kg CO2 year in Cinnamomum camphora [50], and 56.8 kg CO2 year in Alstonia scholaris [51]. However, several studies showed similar CO2 assimilation to that of −1 −1 Z. serrata, such as 18.0 kg CO2 year in Pongamia pinnata [52] and 19.1 kg CO2 year in Pte- rocarpus indicus [17]. The estimation methods of CO2 assimilation in the studies mentioned above were also based on photosynthesis. The difference of annual CO2 assimilation may be affected by interspeciﬁc differences, regional climate, and seasonal climatic shifts [53–55]. −1 −1 Our study found a lower amount of CO2 assimilation (23.79 kg tree year ) than another study (96.14 kg tree−1 year−1)[26] conducted in secondary forest in central Taiwan. The difference may be caused by the poor site quality, including shallow soils, high gravel content, long-term tillage, and prolonged drought in our study site. Plantations in shallow and rocky soils may have a lower growth rate and carbon accumulation potential. For example, the aboveground biomass of Z. serrata in deep and shallow soils was 32 Mg C ha−1 and 9 Mg C ha−1, respectively [48]. The balance between CO2 sink and source during drought is important because future drought in tropical regions may increase in frequency and severity because of global climate change [55]. These adverse weather conditions cause a reduction in tree growth, showing the serious impact of such climate extremes on carbon sequestration in dry tropical forest ecosystems [56]. Collectively, the results of the present study offer insights on photosynthesis and carbon assimilation dynamics in Z. serrata plantations, which could facilitate the assessment and management of plantation species under projected climate change.

5. Conclusions Forests play a crucial role in terrestrial carbon budgets under climate change and provide several important ecosystem service functions. Extreme weather events such as prolonged drought and high temperature strongly impact the growth and carbon sink capacity of plantations. High VPD and high temperature conditions considerably inﬂuence the net photosynthesis rate of Z. serrata plantations in the dry season. However, the gas exchange rate recovered over the following morning, showing that the environmental stresses may not permanently suppress carbon assimilation by plantations. The environmental stress factors that are prominent during the dry season suppressed carbon assimilation Forests 2021, 12, 467 11 of 13

by 43%. Additionally, the relatively low carbon assimilation by Z. serrata plantation at the study site could be attributed to poor site quality.

Author Contributions: C.-I.C., Y.-C.C. and Y.-N.W. conceived of the study, and participated in its design and coordination, and helped to draft the manuscript. C.-I.C., H.-H.L. and J.-C.Y. collected, analyzed, and interpreted data. C.-I.C. and C.-W.W. wrote the manuscript. C.-W.W. and Y.-N.W. advised throughout. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We thank the researchers at the Experimental Forest, National Taiwan University, for their help with data collection and field measurements. We also express our gratitude to the members of the Silviculture Laboratory, School of Forestry and Resource Conservation, National Taiwan University, for their assistance and cooperation. Conflicts of Interest: The authors declare no conflict of interest.

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